Multi-objective particle swarm optimization algorithm based on the disturbance operation

Authors:
Yuelin Gao;Min Qu
Affiliations:
Institute of Information & System Science, Beifang University of Nationalities, Ningxia, Yinchuan, China;Institute of Information & System Science, Beifang University of Nationalities, Ningxia, Yinchuan, China
Venue:
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Year:
2011

Citing 7
Cited 0

Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms

Proceedings of the 1st International Conference on Genetic Algorithms
Muiltiobjective optimization using nondominated sorting in genetic algorithms

Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach

IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization

IEEE Transactions on Evolutionary Computation
A Multiobjective Memetic Algorithm Based on Particle Swarm Optimization

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

To overcome the defect of wide-ranged exploration for particle swarm optimization, a kind of multi-objective particle swarm optimization algorithm with disturbance operation(MPSOD) is proposed. It employs particle swarm optimization and disturbance operation to generate new population in order to enhance the wide-ranged exploration for particle swarm optimization algorithm. Numerical experiments are compared with NSGA-II, SPEA2 and MOPSO on six benchmark problems. The numerical results show the effectiveness of the proposed MPSOD algorithm.